In [13]:
import matplotlib.pyplot as plt
import seaborn as sns
# List of columns to use as x-axis in scatter plots
columns_to_plot = [
'unemployment_rate', 'perc_less_than_high_school', 'perc_high_school',
'perc_higher_than_high_school', 'income_less_than_high_school',
'income_high_school', 'income_higher_than_high_school', 'poverty_rate',
'Gini_Index', 'uninsured_rate', 'SMOCAPI_rate', 'GRAPI_rate'
]
# Set up a 4x4 subplot grid
fig, axes = plt.subplots(4, 3, figsize=(20, 20))
fig.subplots_adjust(hspace=0.4, wspace=0.4) # Adjust spacing between subplots
# Dictionary to store correlation coefficients for each column
correlations = {}
# Loop through each column and plot a scatterplot in the respective subplot
for i, col in enumerate(columns_to_plot):
ax = axes[i // 3, i % 3] # Select the appropriate subplot in the 4x4 grid
sns.scatterplot(x=col, y='offense_rate_per_person', data=df, ax=ax) # Plot scatterplot
# Set title for each subplot
ax.set_title(f'{col} vs. Offense Rate')
# Add state abbreviation labels for each data point
for j in range(df.shape[0]):
ax.text(df[col].iloc[j],
df['offense_rate_per_person'].iloc[j],
df['state_abbv'].iloc[j],
fontsize=9, ha='left')
# Calculate correlation between the column and offense rate
correlation = df[col].corr(df['offense_rate_per_person'])
correlations[col] = correlation # Store the correlation coefficient
# Display the correlation coefficient below each subplot
ax.text(0.5, -0.2, f'Correlation: {correlation:.4f}',
ha='center', va='center', transform=ax.transAxes, fontsize=10)
# Show the entire plot with all subplots
plt.show()
